Search Results for author: Shuhang Chen

Found 6 papers, 0 papers with code

The ODE Method for Asymptotic Statistics in Stochastic Approximation and Reinforcement Learning

no code implementations27 Oct 2021 Vivek Borkar, Shuhang Chen, Adithya Devraj, Ioannis Kontoyiannis, Sean Meyn

In addition to standard Lipschitz bounds on $f$, and conditions on the vanishing step-size sequence $\{\alpha_n\}$, it is assumed that the associated ODE is globally asymptotically stable with stationary point denoted $\theta^*$, where $\bar f(\theta)=E[f(\theta,\Phi)]$ with $\Phi\sim\pi$.

reinforcement-learning

Tracking Fast Neural Adaptation by Globally Adaptive Point Process Estimation for Brain-Machine Interface

no code implementations27 Jul 2021 Shuhang Chen, Xiang Zhang, Xiang Shen, Yifan Huang, Yiwen Wang

In order to identify the active neurons in brain control and track their tuning property changes, we propose a globally adaptive point process method (GaPP) to estimate the neural modulation state from spike trains, decompose the states into the hyper preferred direction and reconstruct the kinematics in a dual-model framework.

Accelerating Optimization and Reinforcement Learning with Quasi-Stochastic Approximation

no code implementations30 Sep 2020 Shuhang Chen, Adithya Devraj, Andrey Bernstein, Sean Meyn

(ii) With gain $a_t = g/(1+t)$ the results are not as sharp: the rate of convergence $1/t$ holds only if $I + g A^*$ is Hurwitz.

reinforcement-learning

Zap Q-Learning With Nonlinear Function Approximation

no code implementations NeurIPS 2020 Shuhang Chen, Adithya M. Devraj, Fan Lu, Ana Bušić, Sean P. Meyn

Based on multiple experiments with a range of neural network sizes, it is found that the new algorithms converge quickly and are robust to choice of function approximation architecture.

OpenAI Gym Q-Learning +1

Zap Q-Learning for Optimal Stopping Time Problems

no code implementations25 Apr 2019 Shuhang Chen, Adithya M. Devraj, Ana Bušić, Sean P. Meyn

The objective in this paper is to obtain fast converging reinforcement learning algorithms to approximate solutions to the problem of discounted cost optimal stopping in an irreducible, uniformly ergodic Markov chain, evolving on a compact subset of $\mathbb{R}^n$.

Q-Learning

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